Stratospheric ozone loss-induced cloud effects lead to less surface ultraviolet radiation over the Siberian Arctic in spring
Bibliographic record
Abstract
Abstract Surface ultraviolet (UV) radiation has important effects on human health, terrestrial and aquatic ecosystems, and air pollution. Both stratospheric ozone and clouds are key factors that influence surface UV radiation. Here we find that Arctic ozone loss may lead to a decrease in surface UV radiation over the Siberian Arctic in spring using ERA5 reanalysis. It is found that Arctic ozone loss is associated with an increase in high clouds by modifying static stability in the upper troposphere. Stratospheric ozone loss allows more UV radiation to reach the surface. On the contrary, the increase in high clouds results in a reduction of surface UV radiation. Interestingly, a composite analysis suggests that this cloud masking effect is found to be stronger than that from stratospheric ozone loss over the Siberian Arctic in spring. These results suggest that we should pay more attention to the high-ozone events which would lead to more surface UV radiation by the cloud effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".